An Energy Management Approach Based on the Use of Shared Electrical Energy Storage System

In this study, an energy management approach based on a forecasting algorithm is proposed in order to minimize the total energy cost of a certain number of residential consumers using a shared electrical energy storage system and located in the same area, and to reduce the peak load demand in the distribution network to which these houses are connected. The proposed method aims to utilize the electricity produced by the houses at the highest level by taking into account the amounts of electricity production and consumption of houses with photovoltaic (PV) panels of different powers, forecasts of these values at different time scales and variable prices of a real-time pricing electricity tariff. For this purpose, the production of each house is used primarily to meet the consumption of the house itself. In case of surplus production, the produced energy is used for the consumption of other houses in the area, stored in the shared storage system or sold to the network by taking the mentioned variables into account. According to the proposed approach, houses earn energy credits in proportion to the amount of energy they provide to the grid or other houses in the area, and they obtain a significant financial gain by using the corresponding amount of energy from the shared energy storage system, especially in the periods of high electricity purchase price. In the simulation studies carried out by using real load demand and PV power production data for a certain number of residential consumers, the comparisons are performed with the results obtained in the absence of a shared energy storage system and the results obtained in case the storage system exists but the relevant forecast values are not taken into consideration. These comparisons have shown that the proposed energy management approach based on the use of shared energy storage system and forecasting algorithm is effective in reducing the energy cost for the end user and limiting the peak load demand for the distribution system operator